Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy
Unlike the high imaging radiation dose of computed tomography (CT), cone-beam CT (CBCT) has smaller radiation dose and presents less harm to patients. Therefore, CBCT is often used for target delineation, dose planning, and postoperative evaluation in the image-guided radiotherapy (IGRT) of various...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/8/4675 |
| _version_ | 1797606569326673920 |
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| author | Lisiqi Xie Kangjian He Dan Xu |
| author_facet | Lisiqi Xie Kangjian He Dan Xu |
| author_sort | Lisiqi Xie |
| collection | DOAJ |
| description | Unlike the high imaging radiation dose of computed tomography (CT), cone-beam CT (CBCT) has smaller radiation dose and presents less harm to patients. Therefore, CBCT is often used for target delineation, dose planning, and postoperative evaluation in the image-guided radiotherapy (IGRT) of various cancers. In the process of IGRT, CBCT images usually need to be collected multiple times in a radiotherapy stage for postoperative evaluation. The effectiveness of radiotherapy is measured by comparing and analyzing the registered CBCT and the source CT image obtained before radiotherapy. Hence, the registration of CBCT and CT is the most important step in IGRT. CBCT images usually have poor visual effects due to the small imaging dose used, which adversely affects the registration performance. In this paper, we propose a novel adaptive visual saliency feature enhancement method for CBCT in IGRT. Firstly, we denoised CBCT images using a structural similarity based low-rank approximation model (SSLRA) and then enhanced the denoised results with a visual saliency feature enhancement (VSFE)-based method. Experimental results show that the enhancement performance of the proposed method is superior to the comparison enhancement algorithms in visual objective comparison. In addition, the extended experiments prove that the proposed enhancement method can improve the registration accuracy of CBCT and CT images, demonstrating their application prospects in IGRT-based cancer treatment. |
| first_indexed | 2024-03-11T05:18:08Z |
| format | Article |
| id | doaj.art-93c8d0e599e4465c8447f444f37cc6c0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:18:08Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-93c8d0e599e4465c8447f444f37cc6c02023-11-17T18:07:34ZengMDPI AGApplied Sciences2076-34172023-04-01138467510.3390/app13084675Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided RadiotherapyLisiqi Xie0Kangjian He1Dan Xu2School of Information Science and Engineering, Yunnan University, Kunming 650500, ChinaSchool of Information Science and Engineering, Yunnan University, Kunming 650500, ChinaSchool of Information Science and Engineering, Yunnan University, Kunming 650500, ChinaUnlike the high imaging radiation dose of computed tomography (CT), cone-beam CT (CBCT) has smaller radiation dose and presents less harm to patients. Therefore, CBCT is often used for target delineation, dose planning, and postoperative evaluation in the image-guided radiotherapy (IGRT) of various cancers. In the process of IGRT, CBCT images usually need to be collected multiple times in a radiotherapy stage for postoperative evaluation. The effectiveness of radiotherapy is measured by comparing and analyzing the registered CBCT and the source CT image obtained before radiotherapy. Hence, the registration of CBCT and CT is the most important step in IGRT. CBCT images usually have poor visual effects due to the small imaging dose used, which adversely affects the registration performance. In this paper, we propose a novel adaptive visual saliency feature enhancement method for CBCT in IGRT. Firstly, we denoised CBCT images using a structural similarity based low-rank approximation model (SSLRA) and then enhanced the denoised results with a visual saliency feature enhancement (VSFE)-based method. Experimental results show that the enhancement performance of the proposed method is superior to the comparison enhancement algorithms in visual objective comparison. In addition, the extended experiments prove that the proposed enhancement method can improve the registration accuracy of CBCT and CT images, demonstrating their application prospects in IGRT-based cancer treatment.https://www.mdpi.com/2076-3417/13/8/4675cone beam CTfeature enhancementlow-rank approximationimage-guided radiotherapy |
| spellingShingle | Lisiqi Xie Kangjian He Dan Xu Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy Applied Sciences cone beam CT feature enhancement low-rank approximation image-guided radiotherapy |
| title | Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy |
| title_full | Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy |
| title_fullStr | Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy |
| title_full_unstemmed | Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy |
| title_short | Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy |
| title_sort | adaptive visual saliency feature enhancement of cbct for image guided radiotherapy |
| topic | cone beam CT feature enhancement low-rank approximation image-guided radiotherapy |
| url | https://www.mdpi.com/2076-3417/13/8/4675 |
| work_keys_str_mv | AT lisiqixie adaptivevisualsaliencyfeatureenhancementofcbctforimageguidedradiotherapy AT kangjianhe adaptivevisualsaliencyfeatureenhancementofcbctforimageguidedradiotherapy AT danxu adaptivevisualsaliencyfeatureenhancementofcbctforimageguidedradiotherapy |